






import datasets

from .evaluate import evaluate


snake_case__   :						int													=   """\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

snake_case__   :						Union[str, Any]													=   """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

snake_case__   :						Any													=   """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,							_KWARGS_DESCRIPTION     )
class     _a							(	datasets.Metric     ):






  """simple docstring"""





  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     return datasets.MetricInfo(
         description=_DESCRIPTION		,		citation=_CITATION		,		inputs_description=_KWARGS_DESCRIPTION		,		features=datasets.Features(
             {
                 'predictions': {
                     'id': datasets.Value('string'			),
                     'prediction_text': datasets.features.Sequence(datasets.Value('string'			)			),
                 },
                 'references': {
                     'id': datasets.Value('string'			),
                     'answers': datasets.features.Sequence(
                         {
                             'text': datasets.Value('string'			),
                             'answer_start': datasets.Value('int32'			),
                         }			),
                 },
             }			)		,		codebase_urls=['https://www.atticusprojectai.org/cuad']		,		reference_urls=['https://www.atticusprojectai.org/cuad']		,		)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     UpperCamelCase_						=  {prediction['id']: prediction['prediction_text'] for prediction in predictions}
     UpperCamelCase_						=  [
         {
             'paragraphs': [
                 {
                     'qas': [
                         {
                             'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
                             'id': ref['id'],
                         }
                         for ref in references
                     ]
                 }
             ]
         }
     ]
     UpperCamelCase_						=  evaluate(dataset=_UpperCAmelCase		,		predictions=_UpperCAmelCase			)
     return score







import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


__lowerCAmelCase     :	Optional[Any]       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	List[str]       			=  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase     :	int       			=  {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__lowerCAmelCase     :	Any       			=  {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
__lowerCAmelCase     :	List[Any]       			=  {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCAmelCase     :	Any       			=  {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
__lowerCAmelCase     :	Optional[int]       			=  {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
__lowerCAmelCase     :	Optional[int]       			=  {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


__lowerCAmelCase     :	List[str]       			=  {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
__lowerCAmelCase     :	str       			=  {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
__lowerCAmelCase     :	Dict       			=  {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						VOCAB_FILES_NAMES
  a__       			=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a__       			=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__       			=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
  a__       			=						DPRContextEncoderTokenizer







class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						VOCAB_FILES_NAMES
  a__       			=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
  a__       			=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__       			=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
  a__       			=						DPRQuestionEncoderTokenizer







__lowerCAmelCase     :	Optional[int]       			=  collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

__lowerCAmelCase     :	Union[str, Any]       			=  collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


__lowerCAmelCase     :	str       			=  R'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '




@add_start_docstrings(_A	)
class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __call__(					self     :					Tuple     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Optional[str] = None     ,				UpperCamelCase__     :					Optional[str] = None     ,				UpperCamelCase__     :					Union[bool, str] = False     ,				UpperCamelCase__     :					Union[bool, str] = False     ,				UpperCamelCase__     :					Optional[int] = None     ,				UpperCamelCase__     :					Optional[Union[str, TensorType]] = None     ,				UpperCamelCase__     :					Optional[bool] = None     ,				**UpperCamelCase__     :					List[str]     ,				)     ->      BatchEncoding:







    """simple docstring"""






    if titles is None and texts is None:
      return super().__call__(
          UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    elif titles is None or texts is None:
      __magic_name__								=			titles if texts is None else texts
      return super().__call__(
          UpperCamelCase__     ,				UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				return_tensors=UpperCamelCase__     ,				return_attention_mask=UpperCamelCase__     ,				**UpperCamelCase__     ,				)
    __magic_name__								=			titles if not isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else [titles]
    __magic_name__								=			texts if not isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else [texts]
    __magic_name__								=			len(UpperCamelCase__       )
    __magic_name__								=			questions if not isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else [questions] * n_passages
    assert len(UpperCamelCase__       ) == len(
        UpperCamelCase__       ), F'''There should be as many titles than texts but got {len(UpperCamelCase__       )} titles and {len(UpperCamelCase__       )} texts.'''
    __magic_name__								=			super().__call__(UpperCamelCase__     ,				UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__       )["""input_ids"""]
    __magic_name__								=			super().__call__(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__     ,				padding=UpperCamelCase__     ,				truncation=UpperCamelCase__       )["""input_ids"""]
    __magic_name__								=			{
        """input_ids""": [
            (encoded_question_and_title + encoded_text)[:max_length]
            if max_length is not None and truncation
            else encoded_question_and_title + encoded_text
            for encoded_question_and_title, encoded_text in zip(UpperCamelCase__     ,				UpperCamelCase__       )
        ]
    }
    if return_attention_mask is not False:
      __magic_name__								=			[]
      for input_ids in encoded_inputs["input_ids"]:
        attention_mask.append([int(input_id != self.pad_token_id       ) for input_id in input_ids]       )
      __magic_name__								=			attention_mask
    return self.pad(UpperCamelCase__     ,				padding=UpperCamelCase__     ,				max_length=UpperCamelCase__     ,				return_tensors=UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					BatchEncoding     ,				UpperCamelCase__     :					DPRReaderOutput     ,				UpperCamelCase__     :					int = 16     ,				UpperCamelCase__     :					int = 64     ,				UpperCamelCase__     :					int = 4     ,				)     ->      List[DPRSpanPrediction]:







    """simple docstring"""






    __magic_name__								=			reader_input["""input_ids"""]
    __magic_name__	,       __magic_name__	,       __magic_name__								=			reader_output[:3]
    __magic_name__								=			len(UpperCamelCase__       )
    __magic_name__								=			sorted(range(UpperCamelCase__       )     ,				reverse=UpperCamelCase__     ,				key=relevance_logits.__getitem__       )
    __magic_name__								=			[]
    for doc_id in sorted_docs:
      __magic_name__								=			list(input_ids[doc_id]       )
      # assuming question & title information is at the beginning of the sequence
      __magic_name__								=			sequence_ids.index(self.sep_token_id     ,				2       ) + 1  # second sep id
      if sequence_ids[-1] == self.pad_token_id:
        __magic_name__								=			sequence_ids.index(self.pad_token_id       )
      else:
        __magic_name__								=			len(UpperCamelCase__       )

      __magic_name__								=			self._get_best_spans(
          start_logits=start_logits[doc_id][passage_offset:sequence_len]     ,				end_logits=end_logits[doc_id][passage_offset:sequence_len]     ,				max_answer_length=UpperCamelCase__     ,				top_spans=UpperCamelCase__     ,				)
      for start_index, end_index in best_spans:
        start_index += passage_offset
        end_index += passage_offset
        nbest_spans_predictions.append(
            DPRSpanPrediction(
                span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]     ,				relevance_score=relevance_logits[doc_id]     ,				doc_id=UpperCamelCase__     ,				start_index=UpperCamelCase__     ,				end_index=UpperCamelCase__     ,				text=self.decode(sequence_ids[start_index : end_index + 1]       )     ,				)       )
      if len(UpperCamelCase__       ) >= num_spans:
        break
    return nbest_spans_predictions[:num_spans]





  def 				_lowercase				(					self     :					Optional[int]     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					int     ,				)     ->      List[DPRSpanPrediction]:







    """simple docstring"""






    __magic_name__								=			[]
    for start_index, start_score in enumerate(UpperCamelCase__       ):
      for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]       ):
        scores.append(((start_index, start_index + answer_length), start_score + end_score)       )
    __magic_name__								=			sorted(UpperCamelCase__     ,				key=lambda UpperCamelCase__       : x[1]     ,				reverse=UpperCamelCase__       )
    __magic_name__								=			[]
    for (start_index, end_index), score in scores:
      assert start_index <= end_index, F'''Wrong span indices: [{start_index}:{end_index}]'''
      __magic_name__								=			end_index - start_index + 1
      assert length <= max_answer_length, F'''Span is too long: {length} > {max_answer_length}'''
      if any(
          start_index <= prev_start_index <= prev_end_index <= end_index
          or prev_start_index <= start_index <= end_index <= prev_end_index
          for (prev_start_index, prev_end_index) in chosen_span_intervals       ):
        continue
      chosen_span_intervals.append((start_index, end_index)       )

      if len(UpperCamelCase__       ) == top_spans:
        break
    return chosen_span_intervals







@add_end_docstrings(_A	)
class 			UpperCAmelCase_     (					_A    ,			_A	):


  '''simple docstring'''







  a__       			=						VOCAB_FILES_NAMES
  a__       			=						READER_PRETRAINED_VOCAB_FILES_MAP
  a__       			=						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__       			=						READER_PRETRAINED_INIT_CONFIGURATION
  a__       			=						["""input_ids""", """attention_mask"""]
  a__       			=						DPRReaderTokenizer

